论文标题
自适应语音质量意识到复杂的神经网络，用于消除声学回波的对比度学习
Adaptive Speech Quality Aware Complex Neural Network for Acoustic Echo Cancellation with Supervised Contrastive Learning
论文作者
论文摘要
声学回声取消（AEC）旨在消除麦克风信号中的回声，混响和不需要的添加声音，同时保持近端扬声器的言语的质量。本文提出了自适应语音质量复杂的神经网络，以专注于实时声音消除的特定任务。在具体而言，我们提出了一个具有不同阶段的复杂模块化的神经网络，以专注于特征提取，声学分离和掩模优化。此外，我们采用对比度学习框架和新颖的语音质量意识损失功能，以进一步提高表现。该模型经过72小时的预训练，然后进行72小时进行微调。所提出的模型优于最先进的性能。
Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation, and unwanted added sounds from the microphone signal while maintaining the quality of the near-end speaker's speech. This paper proposes adaptive speech quality complex neural networks to focus on specific tasks for real-time acoustic echo cancellation. In specific, we propose a complex modularize neural network with different stages to focus on feature extraction, acoustic separation, and mask optimization receptively. Furthermore, we adopt the contrastive learning framework and novel speech quality aware loss functions to further improve the performance. The model is trained with 72 hours for pre-training and then 72 hours for fine-tuning. The proposed model outperforms the state-of-the-art performance.
